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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/13.gif filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.DS_Store
+*pyc
+.vscode
+__pycache__
+*.egg-info
+
+checkpoints
+results
+backup
+LOG

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,257 @@
+## ___***ToonCrafter: Generative Cartoon Interpolation***___
+<!-- ![](./assets/logo_long.png#gh-light-mode-only){: width="50%"} -->
+<!-- ![](./assets/logo_long_dark.png#gh-dark-mode-only=100x20) -->
+<div align="center">
+
+
+
+</div>
+ 
+## 🔆 Introduction
+
+⚠️ Please check our [disclaimer](#disc) first.
+
+🤗 ToonCrafter can interpolate two cartoon images by leveraging the pre-trained image-to-video diffusion priors. Please check our project page and paper for more information. <br>
+
+
+
+
+
+
+
+### 1.1 Showcases (512x320)
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td>Input starting frame</td>
+        <td>Input ending frame</td>
+        <td>Generated video</td>
+    </tr>
+  <tr>
+  <td>
+    <img src=assets/72109_125.mp4_00-00.png width="250">
+  </td>
+  <td>
+    <img src=assets/72109_125.mp4_00-01.png width="250">
+  </td>
+  <td>
+    <img src=assets/00.gif width="250">
+  </td>
+  </tr>
+
+
+   <tr>
+  <td>
+    <img src=assets/Japan_v2_2_062266_s2_frame1.png width="250">
+  </td>
+  <td>
+    <img src=assets/Japan_v2_2_062266_s2_frame3.png width="250">
+  </td>
+  <td>
+    <img src=assets/03.gif width="250">
+  </td>
+  </tr>
+  <tr>
+  <td>
+    <img src=assets/Japan_v2_1_070321_s3_frame1.png width="250">
+  </td>
+  <td>
+    <img src=assets/Japan_v2_1_070321_s3_frame3.png width="250">
+  </td>
+  <td>
+    <img src=assets/02.gif width="250">
+  </td>
+  </tr> 
+  <tr>
+  <td>
+    <img src=assets/74302_1349_frame1.png width="250">
+  </td>
+  <td>
+    <img src=assets/74302_1349_frame3.png width="250">
+  </td>
+  <td>
+    <img src=assets/01.gif width="250">
+  </td>
+  </tr>
+</table>
+
+### 1.2 Sparse sketch guidance
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td>Input starting frame</td>
+        <td>Input ending frame</td>
+        <td>Input sketch guidance</td>
+        <td>Generated video</td>
+    </tr>
+  <tr>
+  <td>
+    <img src=assets/72105_388.mp4_00-00.png width="200">
+  </td>
+  <td>
+    <img src=assets/72105_388.mp4_00-01.png width="200">
+  </td>
+  <td>
+    <img src=assets/06.gif width="200">
+  </td>
+   <td>
+    <img src=assets/07.gif width="200">
+  </td>
+  </tr>
+
+  <tr>
+  <td>
+    <img src=assets/72110_255.mp4_00-00.png width="200">
+  </td>
+  <td>
+    <img src=assets/72110_255.mp4_00-01.png width="200">
+  </td>
+  <td>
+    <img src=assets/12.gif width="200">
+  </td>
+   <td>
+    <img src=assets/13.gif width="200">
+  </td>
+  </tr>
+
+
+</table>
+
+
+### 2. Applications
+#### 2.1 Cartoon Sketch Interpolation (see project page for more details)
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td>Input starting frame</td>
+        <td>Input ending frame</td>
+        <td>Generated video</td>
+    </tr>
+
+  <tr>
+  <td>
+    <img src=assets/frame0001_10.png width="250">
+  </td>
+  <td>
+    <img src=assets/frame0016_10.png width="250">
+  </td>
+  <td>
+    <img src=assets/10.gif width="250">
+  </td>
+  </tr>
+
+
+   <tr>
+  <td>
+    <img src=assets/frame0001_11.png width="250">
+  </td>
+  <td>
+    <img src=assets/frame0016_11.png width="250">
+  </td>
+  <td>
+    <img src=assets/11.gif width="250">
+  </td>
+  </tr>
+
+</table>
+
+
+#### 2.2 Reference-based Sketch Colorization
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td>Input sketch</td>
+        <td>Input reference</td>
+        <td>Colorization results</td>
+    </tr>
+    
+  <tr>
+  <td>
+    <img src=assets/04.gif width="250">
+  </td>
+  <td>
+    <img src=assets/frame0001_05.png width="250">
+  </td>
+  <td>
+    <img src=assets/05.gif width="250">
+  </td>
+  </tr>
+
+
+   <tr>
+  <td>
+    <img src=assets/08.gif width="250">
+  </td>
+  <td>
+    <img src=assets/frame0001_09.png width="250">
+  </td>
+  <td>
+    <img src=assets/09.gif width="250">
+  </td>
+  </tr>
+
+</table>
+
+
+
+
+
+
+
+## 📝 Changelog
+- [ ] Add sketch control and colorization function.
+- __[2024.05.29]__: 🔥🔥 Release code and model weights.
+- __[2024.05.28]__: Launch the project page and update the arXiv preprint.
+<br>
+
+
+## 🧰 Models
+
+|Model|Resolution|GPU Mem. & Inference Time (A100, ddim 50steps)|Checkpoint|
+|:---------|:---------|:--------|:--------|
+|ToonCrafter_512|320x512| TBD (`perframe_ae=True`)|[Hugging Face](https://huggingface.co/Doubiiu/ToonCrafter/blob/main/model.ckpt)|
+
+
+Currently, our ToonCrafter can support generating videos of up to 16 frames with a resolution of 512x320. The inference time can be reduced by using fewer DDIM steps.
+
+
+
+## ⚙️ Setup
+
+### Install Environment via Anaconda (Recommended)
+```bash
+conda create -n tooncrafter python=3.8.5
+conda activate tooncrafter
+pip install -r requirements.txt
+```
+
+
+## 💫 Inference
+### 1. Command line
+
+Download pretrained ToonCrafter_512 and put the `model.ckpt` in `checkpoints/tooncrafter_512_interp_v1/model.ckpt`.
+```bash
+  sh scripts/run.sh
+```
+
+
+### 2. Local Gradio demo
+
+Download the pretrained model and put it in the corresponding directory according to the previous guidelines.
+```bash
+  python gradio_app.py 
+```
+
+
+
+
+
+
+<!-- ## 🤝 Community Support -->
+
+
+
+<a name="disc"></a>
+## 📢 Disclaimer
+Calm down. Our framework opens up the era of generative cartoon interpolation, but due to the variaity of generative video prior, the success rate is not guaranteed.
+
+⚠️This is an open-source research exploration, instead of commercial products. It can't meet all your expectations.
+
+This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.
+****

configs/inference_512_v1.0.yaml

@@ -0,0 +1,103 @@
+model:
+  target: lvdm.models.ddpm3d.LatentVisualDiffusion
+  params:
+    rescale_betas_zero_snr: True
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.012
+    num_timesteps_cond: 1
+    timesteps: 1000
+    first_stage_key: video
+    cond_stage_key: caption
+    cond_stage_trainable: False
+    conditioning_key: hybrid
+    image_size: [40, 64]
+    channels: 4
+    scale_by_std: False
+    scale_factor: 0.18215
+    use_ema: False
+    uncond_type: 'empty_seq'
+    use_dynamic_rescale: true
+    base_scale: 0.7
+    fps_condition_type: 'fps'
+    perframe_ae: True
+    loop_video: true
+    unet_config:
+      target: lvdm.modules.networks.openaimodel3d.UNetModel
+      params:
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 4
+        - 4
+        dropout: 0.1
+        num_head_channels: 64
+        transformer_depth: 1
+        context_dim: 1024
+        use_linear: true
+        use_checkpoint: True
+        temporal_conv: True
+        temporal_attention: True
+        temporal_selfatt_only: true
+        use_relative_position: false
+        use_causal_attention: False
+        temporal_length: 16
+        addition_attention: true
+        image_cross_attention: true
+        default_fs: 24
+        fs_condition: true
+
+    first_stage_config:
+      target: lvdm.models.autoencoder.AutoencoderKL_Dualref
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+      params:
+        freeze: true
+        layer: "penultimate"
+
+    img_cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+      params:
+        freeze: true
+    
+    image_proj_stage_config:
+      target: lvdm.modules.encoders.resampler.Resampler
+      params:
+        dim: 1024
+        depth: 4
+        dim_head: 64
+        heads: 12
+        num_queries: 16
+        embedding_dim: 1280
+        output_dim: 1024
+        ff_mult: 4
+        video_length: 16

configs/training_1024_v1.0/config.yaml

@@ -0,0 +1,166 @@
+model:
+  pretrained_checkpoint: checkpoints/dynamicrafter_1024_v1/model.ckpt
+  base_learning_rate: 1.0e-05
+  scale_lr: False
+  target: lvdm.models.ddpm3d.LatentVisualDiffusion
+  params:
+    rescale_betas_zero_snr: True
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.012
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: video
+    cond_stage_key: caption
+    cond_stage_trainable: False
+    image_proj_model_trainable: True
+    conditioning_key: hybrid
+    image_size: [72, 128]
+    channels: 4
+    scale_by_std: False
+    scale_factor: 0.18215
+    use_ema: False
+    uncond_prob: 0.05
+    uncond_type: 'empty_seq'
+    rand_cond_frame: true
+    use_dynamic_rescale: true
+    base_scale: 0.3
+    fps_condition_type: 'fps'
+    perframe_ae: True
+
+    unet_config:
+      target: lvdm.modules.networks.openaimodel3d.UNetModel
+      params:
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 4
+        - 4
+        dropout: 0.1
+        num_head_channels: 64
+        transformer_depth: 1
+        context_dim: 1024
+        use_linear: true
+        use_checkpoint: True
+        temporal_conv: True
+        temporal_attention: True
+        temporal_selfatt_only: true
+        use_relative_position: false
+        use_causal_attention: False
+        temporal_length: 16
+        addition_attention: true
+        image_cross_attention: true
+        default_fs: 10
+        fs_condition: true
+
+    first_stage_config:
+      target: lvdm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+      params:
+        freeze: true
+        layer: "penultimate"
+
+    img_cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+      params:
+        freeze: true
+    
+    image_proj_stage_config:
+      target: lvdm.modules.encoders.resampler.Resampler
+      params:
+        dim: 1024
+        depth: 4
+        dim_head: 64
+        heads: 12
+        num_queries: 16
+        embedding_dim: 1280
+        output_dim: 1024
+        ff_mult: 4
+        video_length: 16
+
+data:
+  target: utils_data.DataModuleFromConfig
+  params:
+    batch_size: 1
+    num_workers: 12
+    wrap: false
+    train:
+      target: lvdm.data.webvid.WebVid
+      params:
+        data_dir: <WebVid10M DATA>
+        meta_path: <.csv FILE>
+        video_length: 16
+        frame_stride: 6
+        load_raw_resolution: true
+        resolution: [576, 1024]
+        spatial_transform: resize_center_crop
+        random_fs: true  ## if true, we uniformly sample fs with max_fs=frame_stride (above)
+
+lightning:
+  precision: 16
+  # strategy: deepspeed_stage_2
+  trainer:
+    benchmark: True
+    accumulate_grad_batches: 2
+    max_steps: 100000
+    # logger
+    log_every_n_steps: 50
+    # val
+    val_check_interval: 0.5
+    gradient_clip_algorithm: 'norm'
+    gradient_clip_val: 0.5
+  callbacks:
+    model_checkpoint:
+      target: pytorch_lightning.callbacks.ModelCheckpoint
+      params:
+        every_n_train_steps: 9000 #1000
+        filename: "{epoch}-{step}"
+        save_weights_only: True
+    metrics_over_trainsteps_checkpoint:
+      target: pytorch_lightning.callbacks.ModelCheckpoint
+      params:
+        filename: '{epoch}-{step}'
+        save_weights_only: True
+        every_n_train_steps: 10000 #20000 # 3s/step*2w=
+    batch_logger:
+      target: callbacks.ImageLogger
+      params:
+        batch_frequency: 500
+        to_local: False
+        max_images: 8
+        log_images_kwargs:
+          ddim_steps: 50
+          unconditional_guidance_scale: 7.5
+          timestep_spacing: uniform_trailing
+          guidance_rescale: 0.7

configs/training_1024_v1.0/run.sh

@@ -0,0 +1,37 @@
+# NCCL configuration
+# export NCCL_DEBUG=INFO
+# export NCCL_IB_DISABLE=0
+# export NCCL_IB_GID_INDEX=3
+# export NCCL_NET_GDR_LEVEL=3
+# export NCCL_TOPO_FILE=/tmp/topo.txt
+
+# args
+name="training_1024_v1.0"
+config_file=configs/${name}/config.yaml
+
+# save root dir for logs, checkpoints, tensorboard record, etc.
+save_root="<YOUR_SAVE_ROOT_DIR>"
+
+mkdir -p $save_root/$name
+
+## run
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
+--nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
+./main/trainer.py \
+--base $config_file \
+--train \
+--name $name \
+--logdir $save_root \
+--devices $HOST_GPU_NUM \
+lightning.trainer.num_nodes=1
+
+## debugging
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
+# --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
+# ./main/trainer.py \
+# --base $config_file \
+# --train \
+# --name $name \
+# --logdir $save_root \
+# --devices 4 \
+# lightning.trainer.num_nodes=1

configs/training_512_v1.0/config.yaml

@@ -0,0 +1,166 @@
+model:
+  pretrained_checkpoint: checkpoints/dynamicrafter_512_v1/model.ckpt
+  base_learning_rate: 1.0e-05
+  scale_lr: False
+  target: lvdm.models.ddpm3d.LatentVisualDiffusion
+  params:
+    rescale_betas_zero_snr: True
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.012
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: video
+    cond_stage_key: caption
+    cond_stage_trainable: False
+    image_proj_model_trainable: True
+    conditioning_key: hybrid
+    image_size: [40, 64]
+    channels: 4
+    scale_by_std: False
+    scale_factor: 0.18215
+    use_ema: False
+    uncond_prob: 0.05
+    uncond_type: 'empty_seq'
+    rand_cond_frame: true
+    use_dynamic_rescale: true
+    base_scale: 0.7
+    fps_condition_type: 'fps'
+    perframe_ae: True
+
+    unet_config:
+      target: lvdm.modules.networks.openaimodel3d.UNetModel
+      params:
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 4
+        - 4
+        dropout: 0.1
+        num_head_channels: 64
+        transformer_depth: 1
+        context_dim: 1024
+        use_linear: true
+        use_checkpoint: True
+        temporal_conv: True
+        temporal_attention: True
+        temporal_selfatt_only: true
+        use_relative_position: false
+        use_causal_attention: False
+        temporal_length: 16
+        addition_attention: true
+        image_cross_attention: true
+        default_fs: 10
+        fs_condition: true
+
+    first_stage_config:
+      target: lvdm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+      params:
+        freeze: true
+        layer: "penultimate"
+
+    img_cond_stage_config:
+      target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+      params:
+        freeze: true
+    
+    image_proj_stage_config:
+      target: lvdm.modules.encoders.resampler.Resampler
+      params:
+        dim: 1024
+        depth: 4
+        dim_head: 64
+        heads: 12
+        num_queries: 16
+        embedding_dim: 1280
+        output_dim: 1024
+        ff_mult: 4
+        video_length: 16
+
+data:
+  target: utils_data.DataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 12
+    wrap: false
+    train:
+      target: lvdm.data.webvid.WebVid
+      params:
+        data_dir: <WebVid10M DATA>
+        meta_path: <.csv FILE>
+        video_length: 16
+        frame_stride: 6
+        load_raw_resolution: true
+        resolution: [320, 512]
+        spatial_transform: resize_center_crop
+        random_fs: true  ## if true, we uniformly sample fs with max_fs=frame_stride (above)
+
+lightning:
+  precision: 16
+  # strategy: deepspeed_stage_2
+  trainer:
+    benchmark: True
+    accumulate_grad_batches: 2
+    max_steps: 100000
+    # logger
+    log_every_n_steps: 50
+    # val
+    val_check_interval: 0.5
+    gradient_clip_algorithm: 'norm'
+    gradient_clip_val: 0.5
+  callbacks:
+    model_checkpoint:
+      target: pytorch_lightning.callbacks.ModelCheckpoint
+      params:
+        every_n_train_steps: 9000 #1000
+        filename: "{epoch}-{step}"
+        save_weights_only: True
+    metrics_over_trainsteps_checkpoint:
+      target: pytorch_lightning.callbacks.ModelCheckpoint
+      params:
+        filename: '{epoch}-{step}'
+        save_weights_only: True
+        every_n_train_steps: 10000 #20000 # 3s/step*2w=
+    batch_logger:
+      target: callbacks.ImageLogger
+      params:
+        batch_frequency: 500
+        to_local: False
+        max_images: 8
+        log_images_kwargs:
+          ddim_steps: 50
+          unconditional_guidance_scale: 7.5
+          timestep_spacing: uniform_trailing
+          guidance_rescale: 0.7

configs/training_512_v1.0/run.sh

@@ -0,0 +1,37 @@
+# NCCL configuration
+# export NCCL_DEBUG=INFO
+# export NCCL_IB_DISABLE=0
+# export NCCL_IB_GID_INDEX=3
+# export NCCL_NET_GDR_LEVEL=3
+# export NCCL_TOPO_FILE=/tmp/topo.txt
+
+# args
+name="training_512_v1.0"
+config_file=configs/${name}/config.yaml
+
+# save root dir for logs, checkpoints, tensorboard record, etc.
+save_root="<YOUR_SAVE_ROOT_DIR>"
+
+mkdir -p $save_root/$name
+
+## run
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
+--nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
+./main/trainer.py \
+--base $config_file \
+--train \
+--name $name \
+--logdir $save_root \
+--devices $HOST_GPU_NUM \
+lightning.trainer.num_nodes=1
+
+## debugging
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
+# --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
+# ./main/trainer.py \
+# --base $config_file \
+# --train \
+# --name $name \
+# --logdir $save_root \
+# --devices 4 \
+# lightning.trainer.num_nodes=1

gradio_app.py

@@ -0,0 +1,82 @@
+import os, argparse
+import sys
+import gradio as gr
+from scripts.gradio.i2v_test_application import Image2Video
+sys.path.insert(1, os.path.join(sys.path[0], 'lvdm'))
+
+
+i2v_examples_interp_512 = [
+    ['prompts/512_interp/74906_1462_frame1.png', 'walking man', 50, 7.5, 1.0, 10, 123, 'prompts/512_interp/74906_1462_frame3.png'],
+    ['prompts/512_interp/Japan_v2_2_062266_s2_frame1.png', 'an anime scene', 50, 7.5, 1.0, 10, 789, 'prompts/512_interp/Japan_v2_2_062266_s2_frame3.png'],
+    ['prompts/512_interp/Japan_v2_3_119235_s2_frame1.png', 'an anime scene', 50, 7.5, 1.0, 10, 123, 'prompts/512_interp/Japan_v2_3_119235_s2_frame3.png'],
+]
+
+
+
+
+def dynamicrafter_demo(result_dir='./tmp/', res=512):
+    if res == 1024:
+        resolution = '576_1024'
+        css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px}"""
+    elif res == 512:
+        resolution = '320_512'
+        css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px} #input_img2 {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}"""
+    elif res == 256:
+        resolution = '256_256'
+        css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
+    else:
+        raise NotImplementedError(f"Unsupported resolution: {res}")
+    image2video = Image2Video(result_dir, resolution=resolution)
+    with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
+
+
+
+        with gr.Tab(label='ToonCrafter_320x512'):
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            i2v_input_image = gr.Image(label="Input Image1",elem_id="input_img")
+                        with gr.Row():
+                            i2v_input_text = gr.Text(label='Prompts')
+                        with gr.Row():
+                            i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=50000, step=1, value=123)
+                            i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
+                            i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
+                        with gr.Row():
+                            i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
+                            i2v_motion = gr.Slider(minimum=5, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=10)
+                        i2v_end_btn = gr.Button("Generate")
+                    with gr.Column():
+                        with gr.Row():
+                            i2v_input_image2 = gr.Image(label="Input Image2",elem_id="input_img2")
+                        with gr.Row():
+                            i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
+
+                gr.Examples(examples=i2v_examples_interp_512,
+                            inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_input_image2],
+                            outputs=[i2v_output_video],
+                            fn = image2video.get_image,
+                            cache_examples=False,
+                )
+            i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_input_image2],
+                            outputs=[i2v_output_video],
+                            fn = image2video.get_image
+            )
+
+
+    return dynamicrafter_iface
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+    return parser
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+
+    result_dir = os.path.join('./', 'results')
+    dynamicrafter_iface = dynamicrafter_demo(result_dir)
+    dynamicrafter_iface.queue(max_size=12)
+    #dynamicrafter_iface.launch(max_threads=1)
+    dynamicrafter_iface.launch(server_name='0.0.0.0', server_port=8080, max_threads=1)

lvdm/basics.py

@@ -0,0 +1,100 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+import torch.nn as nn
+from utils.utils import instantiate_from_config
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def nonlinearity(type='silu'):
+    if type == 'silu':
+        return nn.SiLU()
+    elif type == 'leaky_relu':
+        return nn.LeakyReLU()
+
+
+class GroupNormSpecific(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def normalization(channels, num_groups=32):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNormSpecific(num_groups, channels)
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}

lvdm/common.py

@@ -0,0 +1,94 @@
+import math
+from inspect import isfunction
+import torch
+from torch import nn
+import torch.distributed as dist
+
+
+def gather_data(data, return_np=True):
+    ''' gather data from multiple processes to one list '''
+    data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
+    dist.all_gather(data_list, data)  # gather not supported with NCCL
+    if return_np:
+        data_list = [data.cpu().numpy() for data in data_list]
+    return data_list
+
+def autocast(f):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(enabled=True,
+                                     dtype=torch.get_autocast_gpu_dtype(),
+                                     cache_enabled=torch.is_autocast_cache_enabled()):
+            return f(*args, **kwargs)
+    return do_autocast
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+def exists(val):
+    return val is not None
+
+def identity(*args, **kwargs):
+    return nn.Identity()
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+def isimage(x):
+    if not isinstance(x,torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+def shape_to_str(x):
+    shape_str = "x".join([str(x) for x in x.shape])
+    return shape_str
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+ckpt = torch.utils.checkpoint.checkpoint
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        return ckpt(func, *inputs, use_reentrant=False)
+    else:
+        return func(*inputs)

lvdm/data/base.py

@@ -0,0 +1,23 @@
+from abc import abstractmethod
+from torch.utils.data import IterableDataset
+
+
+class Txt2ImgIterableBaseDataset(IterableDataset):
+    '''
+    Define an interface to make the IterableDatasets for text2img data chainable
+    '''
+    def __init__(self, num_records=0, valid_ids=None, size=256):
+        super().__init__()
+        self.num_records = num_records
+        self.valid_ids = valid_ids
+        self.sample_ids = valid_ids
+        self.size = size
+
+        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+
+    def __len__(self):
+        return self.num_records
+
+    @abstractmethod
+    def __iter__(self):
+        pass

lvdm/data/webvid.py

@@ -0,0 +1,202 @@
+import os
+import random
+from tqdm import tqdm
+import pandas as pd
+from decord import VideoReader, cpu
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+
+class WebVid(Dataset):
+    """
+    WebVid Dataset.
+    Assumes webvid data is structured as follows.
+    Webvid/
+        videos/
+            000001_000050/      ($page_dir)
+                1.mp4           (videoid.mp4)
+                ...
+                5000.mp4
+            ...
+    """
+    def __init__(self,
+                 meta_path,
+                 data_dir,
+                 subsample=None,
+                 video_length=16,
+                 resolution=[256, 512],
+                 frame_stride=1,
+                 frame_stride_min=1,
+                 spatial_transform=None,
+                 crop_resolution=None,
+                 fps_max=None,
+                 load_raw_resolution=False,
+                 fixed_fps=None,
+                 random_fs=False,
+                 ):
+        self.meta_path = meta_path
+        self.data_dir = data_dir
+        self.subsample = subsample
+        self.video_length = video_length
+        self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution
+        self.fps_max = fps_max
+        self.frame_stride = frame_stride
+        self.frame_stride_min = frame_stride_min
+        self.fixed_fps = fixed_fps
+        self.load_raw_resolution = load_raw_resolution
+        self.random_fs = random_fs
+        self._load_metadata()
+        if spatial_transform is not None:
+            if spatial_transform == "random_crop":
+                self.spatial_transform = transforms.RandomCrop(crop_resolution)
+            elif spatial_transform == "center_crop":
+                self.spatial_transform = transforms.Compose([
+                    transforms.CenterCrop(resolution),
+                    ])            
+            elif spatial_transform == "resize_center_crop":
+                # assert(self.resolution[0] == self.resolution[1])
+                self.spatial_transform = transforms.Compose([
+                    transforms.Resize(min(self.resolution)),
+                    transforms.CenterCrop(self.resolution),
+                    ])
+            elif spatial_transform == "resize":
+                self.spatial_transform = transforms.Resize(self.resolution)
+            else:
+                raise NotImplementedError
+        else:
+            self.spatial_transform = None
+                
+    def _load_metadata(self):
+        metadata = pd.read_csv(self.meta_path)
+        print(f'>>> {len(metadata)} data samples loaded.')
+        if self.subsample is not None:
+            metadata = metadata.sample(self.subsample, random_state=0)
+   
+        metadata['caption'] = metadata['name']
+        del metadata['name']
+        self.metadata = metadata
+        self.metadata.dropna(inplace=True)
+
+    def _get_video_path(self, sample):
+        rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4')
+        full_video_fp = os.path.join(self.data_dir, 'videos', rel_video_fp)
+        return full_video_fp
+    
+    def __getitem__(self, index):
+        if self.random_fs:
+            frame_stride = random.randint(self.frame_stride_min, self.frame_stride)
+        else:
+            frame_stride = self.frame_stride
+
+        ## get frames until success
+        while True:
+            index = index % len(self.metadata)
+            sample = self.metadata.iloc[index]
+            video_path = self._get_video_path(sample)
+            ## video_path should be in the format of "....../WebVid/videos/$page_dir/$videoid.mp4"
+            caption = sample['caption']
+
+            try:
+                if self.load_raw_resolution:
+                    video_reader = VideoReader(video_path, ctx=cpu(0))
+                else:
+                    video_reader = VideoReader(video_path, ctx=cpu(0), width=530, height=300)
+                if len(video_reader) < self.video_length:
+                    print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})")
+                    index += 1
+                    continue
+                else:
+                    pass
+            except:
+                index += 1
+                print(f"Load video failed! path = {video_path}")
+                continue
+            
+            fps_ori = video_reader.get_avg_fps()
+            if self.fixed_fps is not None:
+                frame_stride = int(frame_stride * (1.0 * fps_ori / self.fixed_fps))
+
+            ## to avoid extreme cases when fixed_fps is used
+            frame_stride = max(frame_stride, 1)
+            
+            ## get valid range (adapting case by case)
+            required_frame_num = frame_stride * (self.video_length-1) + 1
+            frame_num = len(video_reader)
+            if frame_num < required_frame_num:
+                ## drop extra samples if fixed fps is required
+                if self.fixed_fps is not None and frame_num < required_frame_num * 0.5:
+                    index += 1
+                    continue
+                else:
+                    frame_stride = frame_num // self.video_length
+                    required_frame_num = frame_stride * (self.video_length-1) + 1
+
+            ## select a random clip
+            random_range = frame_num - required_frame_num
+            start_idx = random.randint(0, random_range) if random_range > 0 else 0
+
+            ## calculate frame indices
+            frame_indices = [start_idx + frame_stride*i for i in range(self.video_length)]
+            try:
+                frames = video_reader.get_batch(frame_indices)
+                break
+            except:
+                print(f"Get frames failed! path = {video_path}; [max_ind vs frame_total:{max(frame_indices)} / {frame_num}]")
+                index += 1
+                continue
+        
+        ## process data
+        assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}'
+        frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w]
+        
+        if self.spatial_transform is not None:
+            frames = self.spatial_transform(frames)
+        
+        if self.resolution is not None:
+            assert (frames.shape[2], frames.shape[3]) == (self.resolution[0], self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}'
+        
+        ## turn frames tensors to [-1,1]
+        frames = (frames / 255 - 0.5) * 2
+        fps_clip = fps_ori // frame_stride
+        if self.fps_max is not None and fps_clip > self.fps_max:
+            fps_clip = self.fps_max
+
+        data = {'video': frames, 'caption': caption, 'path': video_path, 'fps': fps_clip, 'frame_stride': frame_stride}
+        return data
+    
+    def __len__(self):
+        return len(self.metadata)
+
+
+if __name__== "__main__":
+    meta_path = "" ## path to the meta file
+    data_dir = "" ## path to the data directory
+    save_dir = "" ## path to the save directory
+    dataset = WebVid(meta_path,
+                 data_dir,
+                 subsample=None,
+                 video_length=16,
+                 resolution=[256,448],
+                 frame_stride=4,
+                 spatial_transform="resize_center_crop",
+                 crop_resolution=None,
+                 fps_max=None,
+                 load_raw_resolution=True
+                 )
+    dataloader = DataLoader(dataset,
+                    batch_size=1,
+                    num_workers=0,
+                    shuffle=False)
+
+    
+    import sys
+    sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
+    from utils.save_video import tensor_to_mp4
+    for i, batch in tqdm(enumerate(dataloader), desc="Data Batch"):
+        video = batch['video']
+        name = batch['path'][0].split('videos/')[-1].replace('/','_')
+        tensor_to_mp4(video, save_dir+'/'+name, fps=8)
+